Mitra, Robin  ORCID: https://orcid.org/0000-0001-9584-8044, McGough, Sarah F., Chakraborti, Tapabrata, Holmes, Chris, Copping, Ryan, Hagenbuch, Niels, Biedermann, Stefanie, Noonan, Jack, Lehmann, Brieuc, Shenvi, Aditi, Doan, Xuan Vinh, Leslie, David, Bianconi, Ginestra, Sanchez-Garcia, Ruben, Davies, Alisha, Mackintosh, Maxine, Andrinopoulou, Eleni-Rosalina, Basiri, Anahid, Harbron, Chris and MacArthur, Ben D.
2023.
Learning from data with structured missingness.
Nature Machine Intelligence
5
(1)
, pp. 13-23.
10.1038/s42256-022-00596-z
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Abstract
Missing data are an unavoidable complication in many machine learning tasks. When data are ‘missing at random’ there exist a range of tools and techniques to deal with the issue. However, as machine learning studies become more ambitious, and seek to learn from ever-larger volumes of heterogeneous data, an increasingly encountered problem arises in which missing values exhibit an association or structure, either explicitly or implicitly. Such ‘structured missingness’ raises a range of challenges that have not yet been systematically addressed, and presents a fundamental hindrance to machine learning at scale. Here we outline the current literature and propose a set of grand challenges in learning from data with structured missingness.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Mathematics |
| Publisher: | Nature Research |
| ISSN: | 2522-5839 |
| Date of Acceptance: | 21 November 2022 |
| Last Modified: | 06 May 2023 02:01 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/158506 |
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